Perceptual Analysis of Compressed Dynamic Point Clouds in Virtual Reality Environments

• Josef Augusto Oberdan Souza Silva UFPel https://orcid.org/0000-0003-1722-5145
• Gustavo Rehbein UFPel https://orcid.org/0009-0004-5643-0824
• Adriane Borda UFPel https://orcid.org/0000-0001-6760-6566
• Marcelo Porto UFPel https://orcid.org/0000-0003-3827-3023
• Guilherme Corrêa UFPel https://orcid.org/0000-0002-2739-6194

DOI: https://doi.org/10.5753/semish.2026.22376

Resumo

This study investigated the perceived quality of dynamic point clouds in virtual reality (VR) under different compression scenarios through an immersive subjective experiment. Six compression scenarios were analyzed, including one employing a machine learning-based solution for codec complexity reduction. A VR application, developed according to ITU-T recommendations, was used to conduct the experiments and collect user scores, response times, and visual attention data. The results indicated agreement between most objective and perceived quality metrics, with moderate variability across participants. Geometric metrics, particularly p2plane and p2point, showed stronger association with MOS than bitrate and luminance PSNR, while heatmaps revealed greater gaze concentration in central and visually informative regions of the point clouds. These findings support the development of perceptually optimized compression and rendering strategies for immersive point cloud applications.

Referências

Dumic, E. and da Silva Cruz, L. A. (2023). Subjective quality assessment of v-pcccompressed dynamic point clouds degraded by packet losses. Sensors, 23(12):5623.

Graziosi, D., Nakagami, O., Kuma, S., Zaghetto, A., Suzuki, T., and Tabatabai, A. (2020). An overview of ongoing point cloud compression standardization activities: Videobased (V-PCC) and geometry-based (G-PCC). APSIPA Transactions on Signal and Information Processing, 9:e13.

Gutierrez, J., Perez, P., Orduna, M., Singla, A., Cortes, C., Mazumdar, P., Viola, I., Brunnström, K., Battisti, F., Cieplińska, N., et al. (2021). Subjective evaluation of visual quality and simulator sickness of short 360º videos: Itu-t rec. p. 919. IEEE transactions on multimedia, 24:3087–3100.

Ishihara, S. (1960). Test for color blindness. Kanehara Shuppan, Co. Ltd., Tokyo, Japan.

ISO/IEC (2020). Common test conditions for v3c and V-PCC. 2020.

ITU-R (2023). BT.500 : Methodologies for the subjective assessment of the quality of television images. Recommendation ITU-R BT.500-15, Geneva, Switzerland.

ITU-T (2008). Itu-tp. 910. Subjective video quality assessment methods for multimedia applications, Recommendation ITU-T, 910.

ITU-T (2020). Subjective test methodologies for 360º video on head-mounted displays. International Telecommunication Union, 919.

MPEG (2025). Video point cloud compression - VPCC - mpeg-pcc-tmc2 test model candidate software. [link].

Neuman-Donihue, E., Jarvis, M., and Zhu, Y. (2023). Fastpoints: A state-of-the-art point cloud renderer for unity. arXiv preprint arXiv:2302.05002.

Nguyen, M., Vats, S., Zhou, X., Viola, I., Cesar, P., Timmerer, C., and Hellwagner, H. (2024). Compeq-mr: Compressed point cloud dataset with eye tracking and quality assessment in mixed reality. In Proceedings of the 15th ACM Multimedia systems conference, pages 367–373.

Noether, G. E. et al. (1981). Why kendall tau. Teaching Statistics, 3(2):41–43.

Preda, M. (2020). V-PCC codec description. [link].

Rehbein, G., Santos, C., Corrêa, G., and Porto, M. (2025). Machine learning-based block partitioning for V-PCC encoding acceleration. In 2025 International Conference on Visual Communications and Image Processing (VCIP), pages 1–5. IEEE.

Schober, P., Boer, C., and Schwarte, L. A. (2018). Correlation coefficients: appropriate use and interpretation. Anesthesia & analgesia, 126(5):1763–1768.

Sedgwick, P. (2014). Spearman’s rank correlation coefficient. Bmj, 349.

Singla, A., Wang, S., Göring, S., Rao, R. R. R., Viola, I., Cesar, P., and Raake, A. (2023). Subjective quality evaluation of point clouds using remote testing. In Proceedings of the 2nd international workshop on interactive eXtended reality, pages 21–28.

Sullivan, G. J., Ohm, J.-R., Han, W.-J., and Wiegand, T. (2012). Overview of the high efficiency video coding (HEVC) standard. IEEE Transactions on circuits and systems for video technology, 22(12):1649–1668.

Viola, I., Subramanyam, S., Li, J., and Cesar, P. (2022). On the impact of vr assessment on the quality of experience of highly realistic digital humans: A volumetric video case study. Quality and User Experience, 7(1):3.

Zhou, X., Viola, I., Alexiou, E., Jansen, J., and Cesar, P. (2023). Qava-dpc: Eye-tracking based quality assessment and visual attention dataset for dynamic point cloud in 6 dof. In 2023 IEEE international symposium on mixed and augmented reality (ISMAR), pages 69–78. IEEE.

Zhou, X., Viola, I., Alexiou, E., Jansen, J., and Cesar, P. (2025). Subjective and objective quality assessment for dynamic point cloud with visual attention in 6 dof. ACM Transactions on Multimedia Computing, Communications and Applications, 21(8):1–24.